Radiation therapy is widely used in cancer treatment or therapy. The purpose of radiation therapy is to kill the pathology occurring in an object of interest in a patient body, for example, tumor cells, without damaging the surrounding tissue of the object of interest. This is done by delivering a higher radiation dose to the object of interest of the patient body and a possibly lower radiation dose to healthy organ tissues surrounding the object of interest. However, during the whole treatment process, respiratory motion of the patient body may produce inaccuracy on the position where radiation is applied. Indeed, the position of the object of interest of the patient body may change with the respiratory motion, and even the size and shape of the object of interest of patient body sometimes change with deformation by internal body muscles forces.
Object tracking is a challenge when the object of interest is in the patient body due to body movement, such as body movement caused by respiration. Difficulties in tracking the movement of such an object of interest in the patient body caused by respiration may be an important cause of inaccuracy when the radiation therapy is conducted.
Ultrasound imaging is used to provide images during treatment. A known approach for motion tracking is that markers are put on the skin of the patient. The markers are then tracked using an imaging device such as ultrasound. However, dealing with a large set of images is a challenge in object tracking. Currently, there is no solution available yet for tracking the position of an object in the patient body, because of the high computation complexity needed. Measuring the corresponding surface movement is also not accurate enough to reflect the object movement in the body.
The publication, speeding-up image registration for repetitive motion scenarios, ISBI (1355-1358), disclosed a method for real-time image registration for image sequences of organs subject to breathing. During training phase, the images are registered and then the relationship between the image appearance and the spatial transformation is learned by employing dimensionality reduction to the images and storage of the corresponding displacements. For each image in the application phase, the most similar images in the training set are calculated and a displacement prediction is conducted. The publication, respiration induced fiducial motion tracking in ultrasound using an extended SFA approach, Proc. Of SPIE Vol. 9419, disclosed a method for transferring the pre-estimated target motion extracted from ultrasound image sequences in training stage to online data in real-time. The method is based on extracting feature points of the target object, exploiting low-dimensional description of the feature motion through slow feature analysis, and finding the most similar image frame from training data for estimating current or online object location. For each acquired image, the most similar image is calculated and then a mapping is conducted based on the most similar image for each acquired image. Therefore, the computation complexity is high.